{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dd5ac77",
   "metadata": {},
   "source": [
    "### 受限玻尔兹曼机（RBM）原理与代码实现总结\n",
    "---\n",
    "#### **一、RBM原理概述**\n",
    "受限玻尔兹曼机（Restricted Boltzmann Machine）是一种基于能量的概率图模型，由可见层（Visible Layer）和隐层（Hidden Layer）组成，层内无连接，层间全连接。其核心是通过无监督学习学习数据的潜在特征分布。\n",
    "##### **1. 模型结构**\n",
    "- **可见层（v）**：输入数据的显式表示（如像素值）。\n",
    "- **隐层（h）**：提取的潜在特征。\n",
    "- **权重矩阵（W）**：连接可见层与隐层的权重。\n",
    "- **偏置**：可见层偏置（b）和隐层偏置（c）。\n",
    "##### **2. 能量函数与概率分布**\n",
    "RBM的能量函数定义为：\n",
    "$$\n",
    "E(\\mathbf{v}, \\mathbf{h}) = -\\mathbf{v}^T W \\mathbf{h} - \\mathbf{b}^T \\mathbf{v} - \\mathbf{c}^T \\mathbf{h}\n",
    "$$\n",
    "联合概率分布通过玻尔兹曼分布给出：\n",
    "$$\n",
    "P(\\mathbf{v}, \\mathbf{h}) = \\frac{ e^{-E(\\mathbf{v}, \\mathbf{h})} }{Z}\n",
    "$$\n",
    "其中 $ Z $ 为配分函数（归一化因子）。可见层的边缘分布为：\n",
    "$$\n",
    "P(\\mathbf{v}) = \\sum_{\\mathbf{h}} P(\\mathbf{v}, \\mathbf{h})\n",
    "$$\n",
    "##### **3. 条件独立性**\n",
    "由于层内无连接，给定可见层时隐层条件独立，反之亦然：\n",
    "$$\n",
    "P(h_j=1|\\mathbf{v}) = \\sigma\\left(\\sum_i W_{ij} v_i + c_j\\right)\n",
    "$$\n",
    "$$\n",
    "P(v_i=1|\\mathbf{h}) = \\sigma\\left(\\sum_j W_{ij} h_j + b_i\\right)\n",
    "$$\n",
    "其中 $\\sigma(x) = \\frac{1}{1+e^{-x}}$ 为Sigmoid激活函数。\n",
    "##### **4. 训练目标**\n",
    "通过最大化似然函数学习参数（W, b, c）。目标函数为负对数似然：\n",
    "$$\n",
    "\\mathcal{L} = -\\sum_{\\mathbf{v}} \\log P(\\mathbf{v})\n",
    "$$\n",
    "采用对比散度（CD）算法近似梯度，更新规则为：\n",
    "$$\n",
    "\\Delta W_{ij} = \\epsilon \\left(\\langle v_i h_j \\rangle_{\\text{data}} - \\langle v_i h_j \\rangle_{\\text{recon}}\\right)\n",
    "$$\n",
    "其中 $\\epsilon$ 为学习率，$\\langle \\cdot \\rangle_{\\text{data}}$ 和 $\\langle \\cdot \\rangle_{\\text{recon}}$ 分别为数据分布和重构分布的期望。\n",
    "\n",
    "---\n",
    "#### **二、代码内容概括**\n",
    "代码实现了RBM的训练与可视化，主要包含以下部分：\n",
    "##### **1. 核心类 `RBMRunner`**\n",
    "- **功能**：封装RBM训练、数据增强、可视化及评估流程。\n",
    "- **关键参数**：\n",
    "  - `n_visible`：可见层单元数（默认64）。\n",
    "  - `n_components`：隐层单元数（默认256）。\n",
    "  - `learning_rate`：学习率（默认0.1）。\n",
    "  - `batch_size`：批大小（默认100）。\n",
    "  - `n_epochs`：迭代次数（默认30）。\n",
    "- **设备支持**：自动选择GPU（`cuda`）或CPU。\n",
    "##### **2. 训练流程 (`fit` 方法)**\n",
    "- **初始化RBM**：使用 `RestrictedBoltzmannMachine` 定义可见层与隐层维度。\n",
    "- **优化器**：采用随机梯度下降（SGD）优化参数。\n",
    "- **训练步骤**：\n",
    "  1. **正相（Positive Phase）**：计算隐层激活概率 $ P(\\mathbf{h}|\\mathbf{v}) $。\n",
    "  2. **负相（Negative Phase）**：通过模拟退火采样器（`SimulatedAnnealingOptimizer`）生成重构样本。\n",
    "  3. **目标函数**：最小化能量函数加权重衰减（L2正则化）。\n",
    "  4. **反向传播**：更新权重和偏置。\n",
    "##### **3. 其他内容**\n",
    "\n",
    "- **数据加载 (`load_data` 方法)**\n",
    "\n",
    "    - 数据集：使用 `sklearn.datasets.load_digits`（8x8手写数字图像）。\n",
    "    - 增强：对原始图像进行上下左右平移，扩展数据集。\n",
    "\n",
    "- **可视化输出 (设置`plot_img=True`)**\n",
    "\n",
    "    - 生成样本：展示RBM学习到的数据分布。\n",
    "    - 权重与梯度：可视化权重矩阵及其梯度变化。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f0e9dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import time\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "seed = 42\n",
    "# PyTorch\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(seed)       # 为当前GPU设置\n",
    "    torch.cuda.manual_seed_all(seed)   # 为所有GPU设置\n",
    "# Python\n",
    "random.seed(seed)\n",
    "# NumPy\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89bfdf9c-b6f5-41e8-8891-722220c7cb1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RBM] Pre-training start:\n",
      "Iteration 1, Average Objective: 0.023579\n",
      "jmean 0.03867952525615692 jmax 0.12480003386735916\n",
      "hmean 0.04987050220370293 hmax 0.09960930794477463\n",
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      "jmean 0.04809466004371643 jmax 0.2021804302930832\n",
      "hmean 0.5130907297134399 hmax 0.7503772974014282\n",
      "Iteration 22, Average Objective: 1.157180\n",
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      "jmean 0.05450354143977165 jmax 0.39215582609176636\n",
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      "jmean 0.05088474601507187 jmax 0.5017970204353333\n",
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      "jmean 0.08238707482814789 jmax 1.0259462594985962\n",
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      "[RBM] Epoch 1/2 \tAverage Objective: -3.870853\n",
      "\n",
      "Iteration 1, Average Objective: 0.158577\n",
      "jmean 0.08803126215934753 jmax 0.9959999918937683\n",
      "hmean 0.9354426264762878 hmax 1.0661813020706177\n",
      "Iteration 2, Average Objective: -0.264892\n",
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      "jmean 0.08072027564048767 jmax 1.0691114664077759\n",
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      "[RBM] Epoch 2/2 \tAverage Objective: 6.592801\n",
      "\n",
      "[RBM] Pre-training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chang137/GitHub/myenv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 1000 iteration(s) (status=1):\n",
      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
      "\n",
      "Increase the number of iterations to improve the convergence (max_iter=1000).\n",
      "You might also want to scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM Pipline training completed in 174.93 seconds\n",
      "Logistic regression training completed in 0.27 seconds\n",
      "\n",
      "Logistic regression using RBM features:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       188\n",
      "           1       0.84      0.86      0.85       183\n",
      "           2       0.90      0.90      0.90       172\n",
      "           3       0.88      0.86      0.87       205\n",
      "           4       0.94      0.91      0.92       168\n",
      "           5       0.86      0.85      0.86       183\n",
      "           6       0.94      0.97      0.95       183\n",
      "           7       0.91      0.95      0.93       189\n",
      "           8       0.80      0.78      0.79       160\n",
      "           9       0.81      0.81      0.81       166\n",
      "\n",
      "    accuracy                           0.89      1797\n",
      "   macro avg       0.88      0.88      0.88      1797\n",
      "weighted avg       0.89      0.89      0.89      1797\n",
      "\n",
      "\n",
      "Test Accuracy: 0.8865\n",
      "\n",
      "Logistic regression using raw pixel features:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       188\n",
      "           1       0.61      0.54      0.57       183\n",
      "           2       0.81      0.82      0.82       172\n",
      "           3       0.81      0.80      0.80       205\n",
      "           4       0.89      0.86      0.88       168\n",
      "           5       0.81      0.80      0.81       183\n",
      "           6       0.91      0.94      0.93       183\n",
      "           7       0.85      0.90      0.87       189\n",
      "           8       0.68      0.68      0.68       160\n",
      "           9       0.70      0.72      0.71       166\n",
      "\n",
      "    accuracy                           0.80      1797\n",
      "   macro avg       0.80      0.80      0.80      1797\n",
      "weighted avg       0.80      0.80      0.80      1797\n",
      "\n",
      "\n",
      "Test Accuracy: 0.8041\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rbm_digits import  RBMRunner\n",
    "import kaiwu as kw\n",
    "# 添加license相关信息\n",
    "# kw.license.init(user_id=\"\", sdk_code=\"\")\n",
    "\n",
    "logistic = LogisticRegression(random_state=seed)\n",
    "\n",
    "# 初始化RBM\n",
    "rbm = RBMRunner(\n",
    "    n_visible=8*8,\n",
    "    n_components=128, \n",
    "    learning_rate=0.1, \n",
    "    batch_size=32, \n",
    "    n_epochs=2, \n",
    "    verbose=True, \n",
    "    plot_img=False,\n",
    "    random_state=seed\n",
    ")\n",
    "\n",
    "#加载数据\n",
    "X_train, X_test, y_train, y_test = rbm.load_data(plot_img=True)\n",
    "\n",
    "classifier = Pipeline(steps=[('rbm', rbm),\n",
    "                             ('logistic', logistic)])\n",
    "\n",
    "########## 训练模型 ##########\n",
    "logistic.C = 500.0\n",
    "logistic.max_iter = 1000\n",
    "\n",
    "# 训练 RBM-Logistic Pipeline\n",
    "start_time = time.time()\n",
    "classifier.fit(X_train, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"RBM Pipline training completed in {training_time:.2f} seconds\")\n",
    "\n",
    "# 训练 Logistic regression\n",
    "logistic_classifier = LogisticRegression(C=500.0, max_iter=1000, random_state=seed)\n",
    "\n",
    "start_time = time.time()\n",
    "logistic_classifier.fit(X_train, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Logistic regression training completed in {training_time:.2f} seconds\")\n",
    "\n",
    "########## 评估模型 ##########\n",
    "pip_pred = classifier.predict(X_test)\n",
    "pip_acc = accuracy_score(y_test, pip_pred)\n",
    "print(\"\\nLogistic regression using RBM features:\\n%s\\n\" % (\n",
    "    classification_report(\n",
    "        y_test,\n",
    "        pip_pred)))\n",
    "print(f\"Test Accuracy: {pip_acc:.4f}\")   \n",
    "\n",
    "log_pred = logistic_classifier.predict(X_test)\n",
    "log_acc = accuracy_score(y_test, log_pred)\n",
    "print(\"\\nLogistic regression using raw pixel features:\\n%s\\n\" % (\n",
    "    classification_report(\n",
    "        y_test,\n",
    "        log_pred)))\n",
    "print(f\"Test Accuracy: {log_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e3b4ee9-4c12-46f2-95fb-0cd4ba313c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x700 with 128 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制权重\n",
    "rbm.plot_weights(rbm, save_pdf=False)\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "rbm.plot_confusion_matrix(y_test, log_pred, title_suffix=\"RBM Features\", save_pdf=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad106524-b212-40c0-ac4d-0c66d991a978",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a041528-a9f5-457a-9f4e-62d210e43fae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
